Methodology for adaptive decision--making research on manufacturing process of traditional Chinese medicine based on deep reinforcement learning / 中国中药杂志

The manufacturing process of traditional Chinese medicine is subject to material fluctuation and other uncertain factors which usually cause non-optimal state and inconsistent product quality. Therefore, it is necessary to design and collect the quality-rela-ted physical parameters, process parameters, and equipment parameters in the whole manufacturing process of traditional Chinese medicine for digitization and modeling of the process. In this paper, a method for non-optimal state identification and self-recovering regulation was developed for active quality control in the manufacturing process of traditional Chinese medicine. Moreover, taking vacuum belt drying process as an example, a DQN algorithm-based intelligent decision model was established and verified and the implementation process was also discussed and studied. Thus, the process parameters-based self-optimization strategy discovery and path planning of optimal process control were rea-lized in this study. The results showed that the deep reinforcement learning-based artificial intelligence technology was helpful to improve the product quality consistency, reduce production cost, and increase benefit.

Digital twin based key technologies for intelligent manufacturing of traditional Chinese medicine / 中国中药杂志

In this paper, we first introduced the concept of digital twin(DT) based key technologies for intelligent manufacturing of traditional Chinese medicine(TCM) and applied DT in two case studies of novel extraction equipment for traditional Chinese medicine and drying equipment for Chinese medicine pills to illustrate the advantages of DT in development of new pharmaceutical technology and optimization of pharmaceutical equipment structure. Furthermore, we discussed the feasibility to adopt DT in the production process of TCM for formation of data-driven real-time optimization of production process and dynamic prediction `of operation and maintenance service. The "ruled" production mode based on data and driven by algorithm was constructed to realize the technical scheme of quality perception, evaluation, prediction, intelligent control and intelligent decision-making in product life cycle.